# A simple torch style logger
from __future__ import absolute_import
# import matplotlib.pyplot as plt
import numpy as np

__all__ = ['Logger', 'LoggerMonitor']

# def savefig(fname, dpi=None):
#     dpi = 150 if dpi == None else dpi
#     plt.savefig(fname, dpi=dpi)

# def plot_overlap(logger, names=None):
#     names = logger.names if names == None else names
#     numbers = logger.numbers
#     for _, name in enumerate(names):
#         x = np.arange(len(numbers[name]))
#         plt.plot(x, np.asarray(numbers[name]))
#     return [logger.title + '(' + name + ')' for name in names]

class Logger(object):
    '''Save training process to log file with simple plot function.'''

    def __init__(self, fpath, title=None, resume=False):
        self.file = None
        self.resume = resume
        self.title = '' if title == None else title
        if fpath is not None:
            if resume:
                self.file = open(fpath, 'r')
                name = self.file.readline()
                self.names = name.rstrip().split('\t')
                self.numbers = {}
                for _, name in enumerate(self.names):
                    self.numbers[name] = []

                for numbers in self.file:
                    numbers = numbers.rstrip().split('\t')
                    for i in range(0, len(numbers)):
                        self.numbers[self.names[i]].append(numbers[i])
                self.file.close()
                self.file = open(fpath, 'a')
            else:
                self.file = open(fpath, 'w')

    def set_names(self, names):
        if self.resume:
            pass
        # initialize numbers as empty list
        self.numbers = {}
        self.names = names
        for _, name in enumerate(self.names):
            self.file.write(name)
            self.file.write('\t')
            self.numbers[name] = []
        self.file.write('\n')
        self.file.flush()

    def append(self, numbers):
        assert len(self.names) == len(numbers), 'Numbers do not match names'
        for index, num in enumerate(numbers):
            self.file.write(num) #直接写入字符串
            self.file.write('|')
            self.numbers[self.names[index]].append(num)
        self.file.write('\n')
        self.file.flush()

    # def plot(self, names=None):
    #     names = self.names if names == None else names
    #     numbers = self.numbers
    #     for _, name in enumerate(names):
    #         x = np.arange(len(numbers[name]))
    #         plt.plot(x, np.asarray(numbers[name]))
    #     plt.legend([self.title + '(' + name + ')' for name in names])
    #     plt.grid(True)
    #     # plt.savefig('1.jpg')

    def close(self):
        if self.file is not None:
            self.file.close()


class LoggerMonitor(object):
    '''Load and visualize multiple logs.'''

    def __init__(self, paths):
        '''paths is a distionary with {name:filepath} pair'''
        self.loggers = []
        for title, path in paths.items():
            logger = Logger(path, title=title, resume=True)
            self.loggers.append(logger)

    # def plot(self, names=None):
    #     plt.figure()
    #     plt.subplot(121)
    #     legend_text = []
    #     for logger in self.loggers:
    #         legend_text += plot_overlap(logger, names)
    #     plt.legend(legend_text, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
    #     plt.grid(True)
#
if __name__ == '__main__':
    pass
    # Example
    # logger = Logger( '../saved_dict/log.txt')
    # logger.set_names(['Train loss', 'Valid loss', 'Test loss'])
    #
    # length = 100
    # t = np.arange(length)
    # train_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
    # valid_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
    # test_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
    #
    # for i in range(0, length):
    #     logger.append([train_loss[i], valid_loss[i], test_loss[i]])
    # logger.plot()
    #
    # # Example: logger monitor
    # paths = {
    #     'resadvnet20': '../saved_dict/log.txt'
    # }
    #
    # field = ['Valid Acc.']
    #
    # monitor = LoggerMonitor(paths)
    # monitor.plot(names=field)
    # savefig('val.eps')
